
import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE				:Any									=							logging.get_logger(__name__)

set_seed(770)


SCREAMING_SNAKE_CASE				:Any									=							{
    'c_attn': 'att_proj',
    'c_proj': 'out_proj',
    'c_fc': 'in_proj',
    'transformer.': '',
    'h.': 'layers.',
    'ln_1': 'layernorm_1',
    'ln_2': 'layernorm_2',
    'ln_f': 'layernorm_final',
    'wpe': 'position_embeds_layer',
    'wte': 'input_embeds_layer',
}


SCREAMING_SNAKE_CASE				:Union[str, Any]									=							{
    'text_small': {
        'repo_id': 'suno/bark',
        'file_name': 'text.pt',
    },
    'coarse_small': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse.pt',
    },
    'fine_small': {
        'repo_id': 'suno/bark',
        'file_name': 'fine.pt',
    },
    'text': {
        'repo_id': 'suno/bark',
        'file_name': 'text_2.pt',
    },
    'coarse': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse_2.pt',
    },
    'fine': {
        'repo_id': 'suno/bark',
        'file_name': 'fine_2.pt',
    },
}

SCREAMING_SNAKE_CASE				:List[str]									=							os.path.dirname(os.path.abspath(__file__))
SCREAMING_SNAKE_CASE				:Optional[int]									=							os.path.join(os.path.expanduser('~'), '.cache')
SCREAMING_SNAKE_CASE				:Any									=							os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')


def 		UpperCAmelCase     (      a_						,  a_=False		)						->      Optional[Any]:







				"""simple docstring"""



				__A       =					model_type
				if use_small:
								key += "_small"
				return os.path.join(a_						,  REMOTE_MODEL_PATHS[key]["file_name"]		)


def 		UpperCAmelCase     (      a_						,  a_		)						->      List[str]:







				"""simple docstring"""



				os.makedirs(a_						,  exist_ok=a_		)
				hf_hub_download(repo_id=a_						,  filename=a_						,  local_dir=a_		)


def 		UpperCAmelCase     (      a_						,  a_						,  a_=False						,  a_="text"		)						->      List[Any]:







				"""simple docstring"""



				if model_type == "text":
								__A       =					BarkSemanticModel
								__A       =					BarkSemanticConfig
								__A       =					BarkSemanticGenerationConfig
				elif model_type == "coarse":
								__A       =					BarkCoarseModel
								__A       =					BarkCoarseConfig
								__A       =					BarkCoarseGenerationConfig
				elif model_type == "fine":
								__A       =					BarkFineModel
								__A       =					BarkFineConfig
								__A       =					BarkFineGenerationConfig
				else:
								raise NotImplementedError()
				__A       =					F'''{model_type}_small''' if use_small else model_type
				__A       =					REMOTE_MODEL_PATHS[model_key]
				if not os.path.exists(a_		):
								logger.info(F'''{model_type} model not found, downloading into `{CACHE_DIR}`.'''		)
								_download(model_info["repo_id"]						,  model_info["file_name"]		)
				__A       =					torch.load(a_						,  map_location=a_		)
				# this is a hack
				__A       =					checkpoint["model_args"]
				if "input_vocab_size" not in model_args:
								__A       =					model_args["vocab_size"]
								__A       =					model_args["vocab_size"]
								del model_args["vocab_size"]

				# convert Bark model arguments to HF Bark model arguments
				__A       =					model_args.pop("n_head"		)
				__A       =					model_args.pop("n_embd"		)
				__A       =					model_args.pop("n_layer"		)

				__A       =					ConfigClass(**checkpoint["model_args"]		)
				__A       =					ModelClass(config=a_		)
				__A       =					GenerationConfigClass()

				__A       =					model_generation_config
				__A       =					checkpoint["model"]
				# fixup checkpoint
				__A       =					"_orig_mod."
				for k, v in list(state_dict.items()		):
								if k.startswith(a_		):
												# replace part of the key with corresponding layer name in HF implementation
												__A       =					k[len(a_		) :]
												for old_layer_name in new_layer_name_dict:
																__A       =					new_k.replace(a_						,  new_layer_name_dict[old_layer_name]		)

												__A       =					state_dict.pop(a_		)

				__A       =					set(state_dict.keys()		) - set(model.state_dict().keys()		)
				__A       =					{k for k in extra_keys if not k.endswith(".attn.bias"		)}
				__A       =					set(model.state_dict().keys()		) - set(state_dict.keys()		)
				__A       =					{k for k in missing_keys if not k.endswith(".attn.bias"		)}
				if len(a_		) != 0:
								raise ValueError(F'''extra keys found: {extra_keys}'''		)
				if len(a_		) != 0:
								raise ValueError(F'''missing keys: {missing_keys}'''		)
				model.load_state_dict(a_						,  strict=a_		)
				__A       =					model.num_parameters(exclude_embeddings=a_		)
				__A       =					checkpoint["best_val_loss"].item()
				logger.info(F'''model loaded: {round(n_params/1E6						,  1		)}M params, {round(a_						,  3		)} loss'''		)
				model.eval()
				model.to(a_		)
				del checkpoint, state_dict

				return model


def 		UpperCAmelCase     (      a_						,  a_=False						,  a_="text"		)						->      List[Any]:







				"""simple docstring"""



				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				__A       =					"cpu"  # do conversion on cpu

				__A       =					_get_ckpt_path(a_						,  use_small=a_		)
				__A       =					_load_model(a_						,  a_						,  model_type=a_						,  use_small=a_		)

				# load bark initial model
				__A       =					_bark_load_model(a_						,  "cpu"						,  model_type=a_						,  use_small=a_		)

				if model_type == "text":
								__A       =					bark_model["model"]

				if model.num_parameters(exclude_embeddings=a_		) != bark_model.get_num_params():
								raise ValueError("initial and new models don't have the same number of parameters"		)

				# check if same output as the bark model
				__A       =					5
				__A       =					1_0

				if model_type in ["text", "coarse"]:
								__A       =					torch.randint(2_5_6						,  (batch_size, sequence_length)						,  dtype=torch.int		)
								__A       =					bark_model(a_		)[0]

								__A       =					model(a_		)

								# take last logits
								__A       =					output_new_model_total.logits[:, [-1], :]

				else:
								__A       =					3
								__A       =					8
								__A       =					torch.randint(2_5_6						,  (batch_size, sequence_length, n_codes_total)						,  dtype=torch.int		)

								__A       =					model(a_						,  a_		)
								__A       =					bark_model(a_						,  a_		)

								__A       =					output_new_model_total.logits

				# output difference should come from the difference of self-attention implementation design
				if output_new_model.shape != output_old_model.shape:
								raise ValueError("initial and new outputs don't have the same shape"		)
				if (output_new_model - output_old_model).abs().max().item() > 1E-3:
								raise ValueError("initial and new outputs are not equal"		)

				Path(a_		).mkdir(exist_ok=a_		)
				model.save_pretrained(a_		)


def 		UpperCAmelCase     (      a_						,  a_						,  a_						,  a_						,  a_						,  a_						,  )						->      Union[str, Any]:







				"""simple docstring"""



				__A       =					os.path.join(a_						,  a_		)

				__A       =					BarkSemanticConfig.from_pretrained(os.path.join(a_						,  "config.json"		)		)
				__A       =					BarkCoarseConfig.from_pretrained(os.path.join(a_						,  "config.json"		)		)
				__A       =					BarkFineConfig.from_pretrained(os.path.join(a_						,  "config.json"		)		)
				__A       =					EncodecConfig.from_pretrained("facebook/encodec_24khz"		)

				__A       =					BarkSemanticModel.from_pretrained(a_		)
				__A       =					BarkCoarseModel.from_pretrained(a_		)
				__A       =					BarkFineModel.from_pretrained(a_		)
				__A       =					EncodecModel.from_pretrained("facebook/encodec_24khz"		)

				__A       =					BarkConfig.from_sub_model_configs(
				    a_						,  a_						,  a_						,  a_		)

				__A       =					BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config						,  coarseAcoustic.generation_config						,  fineAcoustic.generation_config		)

				__A       =					BarkModel(a_		)

				__A       =					semantic
				__A       =					coarseAcoustic
				__A       =					fineAcoustic
				__A       =					codec

				__A       =					bark_generation_config

				Path(a_		).mkdir(exist_ok=a_		)
				bark.save_pretrained(a_						,  repo_id=a_						,  push_to_hub=a_		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE				:Optional[int]									=							argparse.ArgumentParser()
					# Required parameters

					parser.add_argument('model_type', type=str, help='text, coarse or fine.')
					parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')

					SCREAMING_SNAKE_CASE				:Union[str, Any]									=							parser.parse_args()

					load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)

import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
		print("""Googling.....""")
		__a    :	Tuple         =    """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
		__a    :	List[str]         =    requests.get(url, headers={"""UserAgent""": UserAgent().random})
		# res.raise_for_status()
		with open("""project1a.html""", """wb""") as out_file:  # only for knowing the class
				for data in res.iter_content(1_0_0_0_0):
						out_file.write(data)
		__a    :	Dict         =    BeautifulSoup(res.text, """html.parser""")
		__a    :	Any         =    list(soup.select(""".eZt8xd"""))[:5]

		print(len(links))
		for link in links:
				if link.text == "Maps":
						webbrowser.open(link.get("""href"""))
				else:
						webbrowser.open(F'''https://google.com{link.get("href")}''')